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Right now, many blockchain AI projects are playing out the same routine—demo effects, parameter benchmarking, performance numbers taking turns. It seems grand, but in reality, they are all following the same path.
But there are also projects taking a completely different approach.
They are not exhausting themselves to hype up the buzz, but doing something that may not seem glamorous, yet is extremely solid:
👉 considers the world fundamentally adversarial rather than naturally friendly.
This is reflected in all aspects. From open-source model architecture to the design logic of inference agents, and even in the keywords repeatedly appearing in research reports—malicious nodes, attack paths, verification crashes—these projects' underlying assumptions always point in the same direction: